Freakonomics
Introduction
Summary & Analysis

LitCharts assigns a color and icon to each theme in Freakonomics, which you can use to track the themes throughout the work.

Incentives

Irrational Behavior, Experts, and “Conventional Wisdom”

Morality and Prescriptive vs. Descriptive Thinking

Nature vs. Nurture

Crime

Summary

Analysis

In the early 1990s, there was an increase in the crime rate in the United Sates. The primary causes of the crime wave, according to eminent criminologists, were “superpredators”: young, aggressive people who had no respect for the law and committed serious, violent crimes without any guilt. Government officials, criminologists, and sociologists alike believed that the country was headed for “deepest chaos” because of the rise of superpredators. Then, unexpectedly, the crime rate fell; in fact, it fell “astoundingly.” By 2000, the murder rate was down to its lowest level in more than three decades. So now the question was: what caused this stunning drop in crime?

One of the strangest social phenomena of modern times has been the declining crime rate of the 1990s. By beginning their book with a discussion of the crime rate of the ‘90s, Levitt and Dubner, the two authors, create a mystery in need of a solution. To “solve the mystery,” they’ll use economics—the study of how humans interact with one another through the exchange of goods and incentives.

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The reason that the crime rate fell in the U.S. in the late 90s “concerned a young woman in Dallas named Norma McCorvey.” In the early 70s, McCorvey—a poor, uneducated, alcoholic—tried to get an abortion. When McCorvey failed to get an abortion, abortion activists took up her cause, making her the lead plaintiff in the famous 1973 Supreme Court case Roe v. Wade, the case that determined that women have the right to have abortions. So what do Norma McCorvey and Roe v. Wade have to do with the crime rate? In the 1990s, the abortion rate was far higher than it had been in the 70s: abortions were now legal. The reason that the crime rate fell in the 90s (the authors argue) is that mothers in impoverished neighborhoods were having fewer children; instead, they were more likely to get abortions. As a result, there were fewer children being born in impoverished neighborhoods with unloving parents, and therefore, fewer children who were likely to grow up to become criminals. But despite the fact that abortion rates in impoverished communities had a huge impact on the crime rate, not a single government official or criminologist brought up abortion when trying to explain the reduction in crime.

This passage establishes one of the most important points in the book: in order to understand large, complicated social phenomena, we must sometimes look to small, seemingly trivial events and people. Norma McCorvey played a tiny yet decisive role in the Supreme Court case that legalized abortion—a case that had dramatic repercussions for the young population of the United States in the 1990s. The passage also establishes another “mystery”—the mystery of why politicians and sociologists didn’t point to the abortion rate as a cause of the declining crime rate. Right away, the book draws a distinction between the facts—mathematical, unbiased, and apolitical—and the political figures who interpret the facts (and who often have to polish their interpretation to fit with a certain political or moral point of view). This suggests another one of the book’s key points: the untrustworthiness of so-called experts.

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As with the crime rate, few people understand how the world of real estate works. Real estate agents claim that they can help their clients by selling a house “aggressively” and getting the best offer possible—a useful service for which they’re paid a “cut” of the price of the house. And yet this claim isn’t necessarily true at all. In order to understand how real estate agents, or any other professionals, work, we must first understand what their incentives and motives truly are. For example, in order to understand how obstetricians work, we must accept that obstetricians have an incentive to treat the most expensive procedures, so that they’re paid the most money—even if these procedures aren’t, strictly speaking, necessary.

The passage proposes a counterintuitive way to talk about real estate agents (or, for that matter, any people who present themselves as “experts”). Instead of focusing on what experts say (i.e., the jargon-filled arguments they use to persuade people to change their behavior), we must look at experts’ incentives—in effect, asking, “What do they have to gain from this transaction?” As we’ll see, one can apply an incentives-based analysis to experts in any field, regardless of the experts’ persuasiveness, gravitas, education, etc.

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What incentives do real estate agents have? The agent’s primary incentive is to make the deal possible on the sale of a home. This is good, because it means that the real estate agent’s incentives align with those of the client (they both want to sell the house for the highest price). However, the agent’s incentives don’t align equally with the client’s incentives. If, for example, a real estate agent manages to sell a house for an extra 10,000 dollars, the agent herself will only make an extra 150 dollars (her cut of the commissions minor taxes). So even though real estate agents might seem to have every incentive to sell their clients’ houses for the highest price, their best course of action is to sell a larger number of houses for an average price, rather than taking the time to sell a small number of houses for the absolute highest price.

One might assume that real estate agents will look out for their clients’ interests, both because of their financial motives and because they’re nice people. However, when we apply mathematical analysis to the real estate business, it becomes clear that real estate agents’ incentives, regardless of their personalities or moral convictions, don’t line up with those of their clients’. In general, studying incentives is a good way to predict how people will behave, even if such a form of analysis can be surprising and even disturbing.

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The authors claim that we can also apply economic methods to the world of politics. Many people maintain that money can be used to “buy” elections. But, technically speaking, it’s not clear if money is really the cause of electoral victory. The authors then take a moment to look at the difference between causation and correlation. Ideally, scientists and economists try to use research to prove that one phenomenon causes another. But often, the research can’t prove causation: it can only prove that there is some positive or negative relationship between two phenomena. For example, there’s an old fable about a czar who learns that the most disease-ridden places in his empire are also the places with the most doctors. The czar, foolishly concluding that doctors cause disease, has all the doctors executed.

The passage introduces an important conceptual distinction between causation and correlation. One reason why so many of Levitt and Dubner’s conclusions seem counterintuitive is that people are used to confusing causation and correlation: because two events occur in close proximity to one another, people irrationally assume that one event must cause the other. (For example, people assume that campaign donations causeelectoral victories). The book will show readers how to avoid logical mistakes of this kind.

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The authors then examine the relationship between campaign donations and electoral results. Often people donate to a candidate because the candidate is already winning the election. Therefore, it can be difficult to say when campaign donations cause electoral victories; sometimes, donations merely correlate with the victories. There is, however, one way to tell the difference between causation and correlation with campaign contributions. If Candidate A runs against Candidate B in two consecutive elections, spending different amounts of money in each, and staying equally popular in both elections, we could convincingly measure the causational influence of campaign contributions on electoral victory. When we apply this technique to electoral data, we reach a surprising conclusion: campaign contributions have a minimal impact on election results. A persuasive, popular candidate will be more likely to get donations, but a lackluster candidate with lots of money to spend is unlikely to win an election, contrary to popular belief.

The methods that Levitt and Dubner use to analyze campaign contributions will be important to the book. One of the problems with analyzing data of all kinds is that there are many different variables that could cause a phenomenon. In the case of campaign contributions, the authors try to isolate one independent variable—campaign contributions—by holding other variables the same (in other words, by studying how the same two candidates perform in consecutive elections). In this way, the authors can attempt to isolate the influence of the independent variable—campaign contributions—on the dependent variable—electoral success.

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There’s another common belief that candidates spend huge amounts on elections. In a single election cycle that includes Presidential, House, and Senate elections, one billion dollars are spent on the election. This might sound like a huge sum, but in fact, Americans spend a billion dollars a year just on chewing gum!

Another form of bias that the authors attempt to correct is the tendency to inflate and exaggerate numbers. Dubner and Levitt try to keep figures in perspective—here, for instance, a billion dollars might sound like a lot of money, until one considers how much money Americans spend on more trivial things.

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This book, the authors claim, will use the techniques borrowed from economics and statistics to analyze the world and reach some surprising conclusions, like the ones they’ve discussed in the introduction so far. Economics is an extremely useful form of human inquiry—but unfortunately, too many people think it’s really dull. In part, this book was written to show how economic tools can be fascinating.

Most people don’t realize that economics can be a useful tool for understanding the way the world works, even in fields far removed from traditional economics. Levitt and Dubner want to teach average people how to use economics to make more informed decisions and eliminate forms of bias.

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The authors note that there are a few general rules to keep in mind when reading this book: 1) “Incentives are the cornerstones of modern life.” As we saw with real estate agents, it can be useful to study human behavior by talking about people’s material motives for acting a certain way. 2) “The conventional wisdom is often wrong.” This book will often ignore conventional wisdom, using math and science to show how little people understand their world. 3) Dramatic effects often have distant, even subtle, causes. 4) Experts use their monopoly on a certain kind of information to help themselves. 5) “Knowing what to measure and how to measure it makes a complicated world much less so.” In general, then, this book will apply economics to topics that often seem too strange or offbeat to be worthy of economic analysis.

The five rules that the authors list here can seem counterintuitive, because they challenge the way that people may be used to thinking about the world. For example, it’s natural to assume that people act a certain way because of their personalities or beliefs. Yet the authors claim that the best way to understand human behavior is to study incentives—in effect, to ask “What do they have to gain?” instead of “What kind of people are they?” Humans also have an irrational tendency to trust large groups and so-called experts. One can use economics to study the world in a rational, unbiased manner, without leaning on experts.